Subsequently, question type is used to define the template for the answers.

for the questions asking for reason and attitude, we generate the answers by summarizing corresponding answer fragments.


To make the answer readable, the constituent sentences in the answer should be ordered properly.

the adjacent sentences should be coherent.


φ(i) is calculated by the KLdivergence between language models of sentence i to Wikipedia articles.


Since the consumer reviews often include multiple sentences, we can directly use the adjacency of these sentences as training samples.

we treat the adjacent sentence pairs in the reviews as training samples (i.e.


(2007) proposed a two-layered classifier for question analysis, and retrieved the answer-fragments by keyword matching.

they first identified the opinion questions, and classified them into six predefined question types, including holder, target, attitude, reason, majority, and yes/no.


If D i is more similar to the test sentence t i , the better performance will be achieved for the larger λ.

if D i consists of only a single sentence t i , the best performance will be obtained when λ goes to infinity.


We also experiment with a number of simple but widely-used geometric and information theoretic distance/similarity measures.

we use Kullback-Leibler (KL) divergence (Kullback and Leibler, 1951), Jensen-Shannon (JS) divergence (Lin, 1991), Euclidean distance and Cosine distance.


(2011), which in turn was inspired by a series of previous techniques (Lu et al., 2008;Liang et al., 2009; following the idea of constructing correspondences between NL and MR in a single probabilistic generative framework.

their approach automatically constructs a PCFG that generates NL sentences from MRs, which indicates how atomic MR constituents are probabilistically related to NL words.


On the other hand, our probabilistic approach also considers relatively low-scoring but useful lexemes, thereby utilizing more semantic concepts in the lexicon.

this explains why our approach obtains higher recall in the evaluation of semantic parsing.


Each y j classifier decides if relation j holds for the given entity tuple, using the mention-level classifications as input.

in the figure: • n is the number of distinct entity tuples in D; • M i is the set of mentions for the ith entity pair; • x is a sentence and z is the latent relation classification for that sentence; • w z is the weight vector for the multi-class mention-level classifier; • k is the number of known relation labels in L; • y j is the top-level classification decision for the entity pair as to whether the jth relation holds; • w j is the weight vector for the binary top-level classifier for the jth relation.


(2011a) proposed models where N i for the ith tuple (e 1 , e 2 ) is defined as: {r j | r j (e 1 , e k ) ∈ D, e k = e 2 , r j / ∈ P i }, which is a subset of L \ P i .

entity e 2 is considered a negative example for relation r j (in the context of entity e 1 ) only if r j exists in the training data with a different value.


Edge scores are now computed over (g, p, x) triples.


Importantly, these sieves do not address verbal mentions.

all verbal mentions are still in singleton clusters after this step.


The second sieve implements the complementary action for event clusters.

it merges two verbal clusters when at least two mentions have the same lemma and at least two mentions have semantic arguments with the same role label and the same lemma.


Thus, we slightly modify the linear chain CRFs by fixing the weights of transition features during training and testing.

weights of impossible transition features (e.g., B→B) are set to −∞, and weights of the other transition features (e.g., E→B) are set to 0.


A parse tree T of sentence x can be factorized into several one-level subtrees, each corresponding to a rule r. In practice, binarization of rules is necessary to obtain cubic parsing time.

the right hand side of each rule should contain no more than 2 states.


, j do 14: Keep B best score bottom ¡ step 1 (Line 14-15): get bottom states 16: For each top state N ¡ step 2 (Line 16-17): get top states 17: 18: end for 19: end for 20: end for Line 0 1 2 3 6 9 14 15 Total Bound(w.r.t.

l) for w i,j with POS tag t, we use Viterbi algorithm to search the optimal POS tags of its left and right words.


Dirichlet processes and Poisson-Dirichlet processes alleviate this problem by using an auxiliary variable trick (Robert and Casella, 2004).

we introduce an auxiliary variable over which we also sample but do not need to record.


The parameters W are automatically trained during the learning process using backpropagation.

at each training update, the model reads an n-gram x = (w 1 , .


We optimized the model's parameters on a word similarity task using 4% of the BNC as development data.

we used WordSim353, a benchmark dataset (Finkelstein et al., 2001), consisting of relatedness judgments (on a scale of 0 to 10) for 353 word pairs.


The experimental results show that the use of chunk-level features can lead to significant performance improvement, and that our approach achieves state-of-the-art performance.

our approach is much better at recognizing long and complicated phrases.


Firstly, these models cannot treat globally a sequence of continuous words as a chunk candidate, and thus cannot inspect the internal structure of the candidate, which is an important aspect of information in modeling phrase chunking.

it makes impossible the use of local indicator function features of the type "the chunk consists of POS tag sequence p 1 ...,p k ".


Secondly, the sequence labeling models cannot capture the correlations between adjacent chunks, which should be informative for the identification of chunk boundaries and types.

we find that some headwords in the sentence are expected to have a stronger dependency relation with their preceding headwords in preceding chunks than with their immediately preceding words within the same chunk.


To make the inference tractable, we first make a first-order Markov assumption on the features used in our model.

we assume that the chunk c i and the corresponding label t i are only associated with the preceding chunk c i-1 and the label t i-1 .


Secondly, a POS tag dictionary is used to record POS tags associated with each chunk type.

for each chunk type, we record all POS tags appearing in this type of chunk in the training corpus.


MIRA updates the parameter vector w with two constraints: (1) the positive example must have a higher score by a given margin, and (2) the change to w should be minimal.

this second constraint is to reduce fluctuations in w. we use a generalized version of MIRA McDonald, 2006) that can incorporate k-best decoding in the update procedure.


Table 4: Comparisons of chunking performance for Chinese NP chunking and text chunking.

for NP chunking task, the F1-score of our approach is improved by 0.88% in comparison with SVMs, the best single system.


From the figure, we can see that the performance gap grows rapidly with the increase of the chunk length.

the gap between the two systems is 27.73% when the length hits 4.


The literature on music analysis is noticeably large, and there are several studies concerning the music's power over emotions (Juslin and Sloboda, 2001), thinking (Rauscher et al., 1993), or physical effort (Karageorghis and Priest, 2008).

there has been significant research in music and psychology focusing on the idea of a parallel between affective cues in music and speech (Sundberg, 1982;Scherer, 1995).


We decided to focus on those features that were successfully used in the past for emotion classification (Strapparava and Mihalcea, 2008).

we use: (1) unigram features obtained from a bag-of-words representation, which are the features typically used by corpusbased methods; and (2) lexicon features, indicating the appartenance of a word to a semantic class defined in manually crafted lexicons, which are often used by knowledge-based methods.


We also derive and use coarse textual features, by using mappings between words and semantic classes.

we use the Lin-guistic Inquiry and Word Count (LIWC) and Word-Net Affect (WA) to derive coarse textual features.


One clear conclusion can be drawn from these experiments: the textual and musical features are both useful for the classification of emotions in songs, and, more importantly, their joint use leads to the highest classification results.

the joint model gives an error rate reduction of 12.9% with respect to the classifier that uses only textual features, and 31.2% with respect to the classifier that uses only musical features.


Table 8 shows the results obtained in previous work for the recognition of emotions in a corpus consisting of 1,000 news headlines (Strapparava and Mihalcea, 2007) annotated for the same six emotions.

the table shows the best overall correlation results obtained by the three emotion recognition systems in the SEMEVAL task on Affective Text (Strapparava and Mihalcea, 2007): (Chaumartin, 2007;Kozareva et al., 2007;Katz et al., 2007).


The bigram-based features outperformed both unigram-based and trigram-based features.

the similarities using the base tag set with bigrams achieved the best performance.


The current work builds on many previous works on the task of monotone substring-based transduction, including spelling correction, letterto-phone conversion and transliteration between different scripts.

our substring-based approach to spelling correction is motivated by the success on transliteration (e.g., Sherif and Kondrak, 2007;Cherry and Suzuki, 2009) and letter-tophoneme conversion (e.g., Jiampojamarn et al., 2007;Rama et al., 2009).


Second, there must not be any alignments from characters inside the aligned phrase to characters outside the phrase.

we do not extract a phrase pair if there is an alignment from within the phrase pair to outside the phrase pair.


p 1 and p 2 are two phrases of a hypothesis's original causality.

p 1 ⊥ q 1 and p 2 ⊥ q 2 are contradiction pairs, and Ct(p 1 , q 1 ) and Ct(p 2 , q 2 ) are their contradiction scores.


Among various options, in this paper, we start from the standard technique proposed by Bannard and Callison-Burch (2005) with bilingual parallel corpora (see also Section 2.2).

we assume the phrase-based SMT framework (Koehn et al., 2003).


Given a set of paraphrase patterns, such as those shown in (4), a set of novel instances, i.e., novel paraphrases, P Hvst , will now be harvested from monolingual non-parallel corpora.

a set of appropriate slot-fillers will be extracted.


For instance, when the full size of bilingual parallel corpus in Patent was used, we acquired 1.41M pairs of seed paraphrases, P Seed , and 28.7M pairs of novel paraphrases, P Hvst .

our method expanded P Seed by about 21 times.


In addition, the linguistic and visual context in which words are heard and used is often noisy and highly ambiguous.

many words in a language are polysemous and have different meanings.


This paper presents a comparative study of graph-based approaches for cross-domain sentiment classification.

the paper analyses two existing methods: an optimisation problem and a ranking algorithm.


(2011) also relies on word co-occurrences.

the authors presented a method for automatic construction of a sentiment-sensitive thesaurus where each lexical element (either unigram or bigram) is connected to a list of related lexical elements which most frequently appear in the context expressing the same sentiment.


We choose SVMs as our main learning technique because they have proved to be the best supervised algorithm for SC (Pang and Lee, 2008).

we use the LIBSVM library (Chang and Lin, 2011) and a linear kernel function to train the classifier.


The isolines image of the baseline accuracy delivers a good representation of domain relations.

we can observe two regions with the highest accuracy (EL-KI, KI-EL) and (BO-DV, DV-BO) and two regions with a big performance drop (EL-BO, EL-DV, KI-BO, KI-DV) and (BO-EL, BO-KI, DV-EL, DV-KI).


In this paper, we also discuss some ideas about how to infer optimal parameter values for the algorithms on the basis of domain characteristics.

the strong correlation for γ with domain similarity and complexity has been observed.


We also lifted several restrictions imposed in previous work (Bethard et al., 2007;Yoshikawa et al., 2009;Verhagen et al., 2010).

we do not require that event mentions and time expressions have to appear in the same sentence, and we do not require two event mentions have to appear very close to each other (e.g., main event mentions in adjacent sentences) in order to be considered as candidate pairs for classification.


Another important improvement over previous work is our global inference model We would like to highlight that our work is also distinct from most previous works in the global inference component.

our global inference model jointly optimizes the E-E relations amongst event mentions and their associations, E-T, with temporal information (intervals in our case).


Given a pair of event mentions, the classifier predicts one of the four temporal relations between them: before,āfter,ōverlap andno relation.

: For training of the classifiers, we define a set of features following some previous work (Bethard et al., 2007;Chambers and Jurafsky, 2008;Yoshikawa et al., 2009), together with some additional features that we believe to be helpful for the interval-based representation.


Finally, to capture the interactions between our local pairwise classifiers we add the following constraints: Intuitively, the inequality constraints in (10) specify that a temporal relation between two event mentions can be inferred from their respective associated time intervals.

if two event mentions e i and e j are associated with two time intervals I k and I l respectively, and I k precedes I l in the timeline, then e i must happen before e j .


It is important to note that our interval-based formulation is more concise in terms of the number of variables and constraints needed in the ILP relative to time expression-based (or timepoint-based) formulations used in previous work (Chambers and Jurafsky, 2008).

in such timepoint-based formulations, the relation between each event mention and each time expression needs to be inferred, resulting in |E||T ||R T | variables, where |E|, |T |, and |R T | are the numbers of event mentions, time points, and temporal relations respectively.


Next, we simply take the probability distribution of that event mention pair as the distribution over the relations, for the event pair.

sumscore: The probability between any mention e ∈ E i and an interval I is obtained by: To obtain the probability distribution over the set of temporal relations, R, for any pair of mentions in E i × E j , we used the following procedure: (e i , e j ), r (e i , e j ) * = arg max (e i ,e j )∈E i ×E jP (e i , e j ), r * p ee,r =P (e i , e j ) * , r , ∀r ∈ R given two groups of event mentions, we first compute the total score of each relation, and select the relation which has the highest score.


Overall, the system that combined event coreference and the global inference model achieved the best performance, which significantly overtook all other compared systems.

it outperformed the baseline system, the local classifiers, and the joint inference model without event coreference with 80%, 25%, and 14% of relative improvement in F 1 , respectively.


We also showed that event coreference can naturally support timeline construction, and good event coreference led to significant improvement in the system performance.

when such gold event coreference knowledge was injected into the model, a significant improvement in the overall performance could be obtained.


this outcome is captured by squaring a shared parameter belonging to the left-fringe determiner The: In these grammars, dependents' attachment probabilities, given heads, are additionally conditioned only on their relative positions -as in traditional models (Klein and Manning, 2004;Paskin, 2001b):  DBM-2 allows different but related grammars to coexist in a single model.

we presuppose that all sentences are assigned to one of two classes: complete and incomplete (comp ∈ {T, F}, for now taken as exogenous).


The generic topic is thus shared across all languages, and will behave quite differently from a languagespecific topic, which is not shared.

there are eight topics, representing seven native language groups that are of interest (Bulgarian, Czech, French, Russian, Spanish, Chinese, and Japanese) and the second language English itself.


, y (T ) ), (y (t) , y (t+1) ) ∈ E by: Lastly, we also assume the existence of node features.

let φ represent a feature mapping from nodes to R D (for example, tf-idf word vectors).


Given this framework, our goal is to develop a probabilistic model over sets of k threads of length T , favoring sets whose threads have large weight but are also distinct from one another with respect to φ.

a high-probability set under the model should include threads that are both salient and diverse.


To overcome this bottleneck, our parse, price and cut algorithm, as shown in algorithm 1, uses column and row generation.

it lazily instantiates the grandparent edge variables z gp g,p,c , and the corresponding cubic number of constraints.


In the following we present an upper bound on the reduced cost, r gp g,p,c ≥ r gp g,p,c , which decomposes in a way that allows for more efficient search.

using this bound, we find all new grandparent edgesN for which this upper bound is positive: Next we prune away all but the grandparent edges for which the exact reduced cost is positive:  we maximize over all possible attribute values v any token g could have, and any possible relation e a token g can have to p and c. Notice that these bounds can be calculated offline, and hence amortize after deployment of the parser.


Recently, Rahman and Ng (2011a) further enriched the ensemble by considering various feature sets and learning models.

three types of feature sets (conventional, lexical and combined) and three learning algorithms (mention-pair model, mention-ranking model and the clustering-ranking model) are employed.


Optimization We determine the vector µ by minimizing the parametric distance (4) between all target labeled documents and their corresponding source labeled document identified in the previous step.

the solution µ to this linear programming problem can be regarded as the cross-domain knowledge between source domain s u and the target domain t. Repeating for every source domain s u , u = 1, .


We employ ACE 2005 corpora to simulate the domain adaptation settings in experiments.

we create six domain adaptation tasks, BC, BN, CTS, NW, UN, WL in total.


The syntactic constraint penalizes ungrammatical parses by encouraging the semantic parser to produce parse trees that agree with a dependency parse of the same sentence.

the syntactic constraint requires the predicate-argument structure of the CCG parse to agree with the predicate-argument structure of the dependency parse.


Our goal in this paper is to examine the resolution of complex cases of definite pronouns that appear in sentences exemplified by the shout example.

each sentence (1) has two clauses separated by a discourse connective (i.e., the connective appears between the two clauses, just like because in the shout example), where the first clause contains two or more candidate antecedents (e.g., Ed and Tim), and the second clause contains the target pronoun (e.g., he); and (2) the target pronoun agrees in gender, number, and semantic class with each candidate antecedent, but does not have any overlap in content words with any of them.


Note that state-ofthe-art pronoun resolvers (e.g., JavaRAP (Qiu et al., 2004), GuiTaR (Poesio and Kabadjov, 2004), as well as those designed by Mitkov (2002) and Charniak and Elsner (2009)) and coreference resolvers (e.g., BART (Versley et al., 2008), CherryPicker (Rahman and Ng, 2009), Reconcile (Stoyanov et al., 2010), the Stanford resolver (Raghunathan et al., 2010;Lee et al., 2011)) cannot accurately resolve the difficult pronouns in these structurally simple sentences, as they do not have the mechanism to capture the fine distinctions between twin sentences.

when given these sentences, the best that the existing resolvers can do to resolve the pronouns is guessing.


We employ machine learning to combine the features derived from different knowledge sources.


3 This step results in one chain being extracted, which contains punisho and escape-s.

the protagonist in this chain is the subject of an escape event and the object of a punish event.


We consider three kinds of polarity-reversing context: negation, comparative adverb, and discourse connective.

we determine whether w A is negated using the Stanford dependency parser, which explic-itly annotates instances of negation; we determine the existence of a comparative adverb (e.g., "more", "less") using the POS tag "RBR"; and we determine whether A exists in a clause headed by a polarityreversing connective, such as although.


After flipping p A by context, we can infer A's rank value from it.

a's rank value is "better" if p a is positive; "worse" if p a is negative; and "cannot be determined" if p a is neutral.


We create three features, HPOL1, HPOL2, and HPOL3, based on our heuristic polarity determination component.

if the rank value of the pronoun or the rank value of one or both of the candidate antecedents cannot be determined, the values of all three binary features will be set to zero for both i 1 and i 2 .


If the count is at least 100, we employ a procedure for heuristically selecting the antecedent for the target anaphor.

if X is a verb, then it resolves the target pronoun to the candidate antecedent that has the same grammatical role as the pronoun.


As before, we create our unannotated corpus using the documents in BLLIP, Reuters, and English Gigaword.

we first parse each sentence in the corpus using the Stanford dependency parser.


SC3 is the same as SC2, except that MI is replaced with corpus frequency.

sC2 and sC3 employ different measures to heuristically predict the correct antecedent for the target pronoun.


Using these statistics, we can then compute the aforementioned features for our example.

sC1 We exploit the coreference-annotated training documents by creating lexical features from them.


While it seems somewhat surprising that a supervised resolver does not perform as well as a rulebased resolver, neither of them employs knowledge sources that are particularly useful for our dataset.

despite given access to annotated data, the Baseline Ranker may not be able to make effective use of it due to the lack of useful features.


Our experimental results indicate that it is a challenge for state-of-theart resolvers, and while we proposed new knowledge sources for addressing this challenge, our resolver still has a lot of room for improvement.

our error analysis indicates that further gains could be achieved via more accurate sentiment analysis and induction of world knowledge from corpora or the Web.


The drawback to the binary model is that the probabilities assigned to the candidate speakers do not need to directly compete against each other.

when assigning a binary probability to a candidate speaker, the classifier does not take into account how good the other candidate speakers are.


As speaker-specific evidence is far too sparse, we encode the speakers with their ordinal position backwards from the quote.

the candidate speaker immediately preceding the quote would be labelled "speaker1", the speaker preceding it would be "speaker2" and so on.


After obtaining individual word assignments z, we can estimate the topic multinomials and the per-document mixing proportions.

the topic multinomials are estimated as: while the per-document mixing proportions fixed can be estimated as: In this section, we draw comparisons with the current state-of-the-art models for hierarchical topic modeling (Blei et al., 2004;Petinot et al., 2011) and show that at certain choices of the parameters of our model, these methods fall out as special cases.


The FScore of the class C r , is the maximum FScore value attained at any node in the hierarchical clustering tree T .

the FScore of the entire clustering solution is then defined to be the sum of the individual class FScore weighted according to the class size.


Secondly, relation extraction has been a common task for pioneering efforts in processing data mined from the internet, and otherwise noisy or out-of-domain data.

large noisily-annotated data sets have been generated by leveraging freely available knowledge bases such as Freebase (Bollacker et al., 2008;Mintz et al., 2009).


However, this basic approach for emission probabilities produces bad unknown-word probabilities.

if for each tag we simply assume one count for each entry in the tag dictionary and one count for unknowns and then normalize, the probability of an unknown word having a specific tag is inversely correlated with the number of word types associated with the tag in the tag dictionary.


Specifically, if for each tag we simply assume one count for each entry in the tag dictionary and one count for unknowns and then normalize, the probability of an unknown word having a specific tag is inversely correlated with the number of word types associated with the tag in the tag dictionary.

a tag that appears with a smaller number of distinct words will be seen by the HMM as being a better candidate tag for an unknown word.


First, we focus on calculating C known (w, t).

if a word w appears C(w) times in the raw corpus, and is seen with |TD(w)| tags in the tag dictionary, then assume for each t in TD(w): and C known (w, t) = 0 for all other t. we split C(w), the count of w tokens in the corpus, evenly among each of w's possible tags.


In this paper, we explore different usages of features and propose an approach which mines cross document information to capture the person specific importance for a feature.

we construct a semantic graph from Wikipedia concepts appearing in all documents that contain the target name (which we refer to name observation set), then group them into several topics and further weight each feature by considering both the relatedness of the feature to its corresponding topic and the importance of this topic in the current name observation set.


Graph Construction In our model, we capture the topic structure through a semantic graph.

for each name observation set, we connect all Wikipedia concepts appearing in the current observation set by their pairwise semantic relatedness- David and Ian (2008)to form a semantic graph.


The underlying idea of the equation is, if two name observations share more and closer common topics, and also these topics receive higher weights according to the current name observation set, then the two observations should be more related to each other.

the factor sim(o 1 ∩ t, o 2 ∩ t) is designed to measure the fine relatedness between o 1 and o 2 given the topic t. Sometimes, both o 1 and o 2 are mapped to t and both close to this topic, but in fact they depict different aspects of t since some of our topics are more general thus include several aspects.


In this paper, we explore the feature space in the web person name disambiguation task and propose a topic-based model which exploits corpus level person specific evidences to handle the data sparsity challenges, especially the case that limited evidences can be collected from the local context.

we harvest topics from wikipedia concepts appearing in the name observation set, and weight a concept based on both the relatedness of the concept to its corresponding topic and the importance of this topic in the current name observation set, so that some discriminative but sparse features can obtain more reliable weights.


As a result, there is a large amount of previous research that handles the problem of reordering through the use of improved reordering models for phrase-based SMT , hierarchical phrase-based translation (Chiang, 2007), syntax-based translation (Yamada and Knight, 2001), or preordering (Xia and McCord, 2004).

systems that use sourcelanguage syntax allow for the handling of longdistance reordering without large increases in The first author is now affiliated with the Nara Institute of Science and Technology.


However, as building a parser for each source language is a resourceintensive undertaking, there has also been some interest in developing reordering rules without the use of a parser (Rottmann and Vogel, 2007;Tromble and Eisner, 2009;DeNero and Uszkoreit, 2011;Visweswariah et al., 2011), and we will follow this thread of research in this paper.

two methods deserve mention for being similar to our approach.


Based on this criterion, we first define a loss L t (F ) that will be higher for orderings that are further from the oracle.

we take the sum of all pairwise orderings that do not follow the expected order where δ(•) is an indicator function that is 1 when its condition is true, and 0 otherwise.


We can further define this sum recursively and use memoization for improved efficiency ( 3)For non-terminal nodes, we first focus on straight non-terminals with parent node d = str, l, c, c + 1, r , and left and right child nodes l, lc, lc+1, c and d r = s r , c+1, rc, rc+ 1, r .

first, we note that the loss for the subtree rooted at d can be expressed as the subtree's total loss can be factored into the loss of its left subtree, the loss of its right subtree, and the additional loss contributed by comparisons between the words spanning both subtrees.


On the one hand, an average word aligner does not consider the syntax information of both languages, and the output links may violate syntactic correspondence.

some SL words yielded by a SL parse tree node may not be traced to, via alignment links, some TL words with legitimate syntactic structure.


Similarly, in Figure 1(b) some parsing errors might be fixed if the parser takes into consideration the correct alignment links about "propaganda" and "lecture".

alignment errors and parsing might be fixed if word aligner and parser are not mutually independent.


Most monolingual parsers used in SSMT are trained upon certain tree bank.

a parser is trained with the target of maximizing the agreement between its decision on syntactic structure and that decision in the human-annotated parse trees.


It is reasonable to combine them, and there are two alternatives of the combination, depending on the order of application.

we could either improve alignment matrix by IDSG and then re-train parser with the better alignment, or re-train parser and then improve alignment matrix with better syntactic information.


Once the transformation sequence has been learned, it can be deterministically applied to any parsed sentences, yielding new parse trees with constituency structures that agree better with the bilingual alignments yet remain consistent across the corpus.

we use this method to learn a transformation sequence for the English trees in a set of English to Chinese MT training data.


the newly created nodes that result from an ARTIC-ULATE or ADOPT transformation cannot then participate as the LEFT or RIGHT argument of a subsequent ARTICULATE transformation or as the AUNT or TARGET argument of a subsequent ADOPT transformation.


Given the amount of variability between the outputs of different parsers and word aligners (or even the same systems with different settings), the best way to improve agreement is to learn a transformation sequence that is specifically tuned for the same annotators (parsers and word aligners) we are evaluating with.

we found that though training on the English Chinese Translation Treebank produces clean, interpretable rules, preliminary experiments showed little to no improvement from using these rules for MT, primarily because actual alignments are not only noisier but also systematically different from gold ones.


There exists, however, a great challenge to compile consistently relations extracted from text by these methods, because they assume a simplifying assumption that relations are time-invariant.

they implicitly disregard the fact that statements in texts actually reflect the state of the world at the time when they were written, which follows that relations extracted from such texts eventually become outdated as the real world evolves over time.


We tackle this problem by using the same technique presented in section 3.1.

we use the fol-4 θ2 = 3000 in our experiment.


To build the DTs effectively, a common assumption is that they are binary trees (Soricut and Marcu, 2003;duVerle and Prendinger, 2009).

multi-nuclear relations (e.g., LIST, JOINT, SEQUENCE) involving more than two EDUs are mapped to a hierarchical right-branching binary tree.


Formally, this can be written as, DT * = argmax DT P (DT |Θ) Our discourse parser implements a probabilistic CKY-like bottom-up algorithm for computing the most likely parse of a sentence using dynamic programming; see (Jurafsky and Martin, 2008) for a description.

with n number of EDUs in a sentence, we use the upper-triangular portion of the n × n Dynamic Programming Table (DPT).


Our experiments on the Instructional corpus consider the same 26 primary relations (e.g., GOAL:ACT, CAUSE:EFFECT, GENERAL-SPECIFIC) used in (Subba and Eugenio, 2009) and also treat the reversals of non-commutative relations as separate relations.

pREpARATION-ACT and ACT-pREpARATION are two different relations.


We use SPADE as our baseline model and apply the same modifications to its default setting as described in (Fisher and Roark, 2007), which delivers improved performance.

in testing, we replace the Charniak parser (Charniak, 2000) with a more accurate reranking parser (Charniak and Johnson, 2005).


We evaluate the segmentation accuracy with respect to the intra-sentential segment boundaries following (Fisher and Roark, 2007).

if a sentence contains n EDUs, which corresponds to n − 1 intra-sentence segment boundaries, we measure the model's ability to correctly identify these n − 1 boundaries.


We use the part-ofspeech (POS) tagger, the named-entity recognizer, the parser (Klein and Manning, 2003), and the coreference resolution system (Lee et al., 2011).

the dependency structures of the parser's output are used for VP-fragment extraction (Sec.


We have carried out a pilot human evaluation on the German data in order to see whether human judges prefer word orders obtained from the lifting-based linearizer.

we wanted to check whether the lifting-based linearizer produces more natural word orders for sentences that had a non-projective tree in the corpus, and maybe less natural word orders on originally projective sentences.


Near the sentence boundaries the appropriate terms were truncated in Equation 3.

at the beginning of the sentence shorter n-gram contexts were used and at the end of the sentence terms beyond the end-of-sentence token were dropped.


We replaced the standard substitution edit operation with three new operations: S word for same word substitution, S lemma for same lemma substitution, and S punc for same punctuation substitution.

all but the three matching-based substitutions are disallowed.


The line of work on probabilistic tree-edit distance models bears a strong connection to this work (McCallum et al., 2005;Bernard et al., 2008;Wang and Manning, 2010;Emms, 2012).

our pFSM model and the log-linear parameterization were inspired by Wang and Manning (2010).


We show that, with heavy caveats, there are such thresholds, though we also discuss the hazards in their use.

many other factors contribute to the significance level, and we investigate several of them.


However, we have demonstrated some limitations of statistical significance testing for NLP.

while statistical significance is usually a minimum necessary condition to demonstrate that a performance difference is real, it's also important to consider the relationship between test set performance and the actual goals of the systems being tested, especially if the system will eventually be used on data from a different domain than the test set used for evaluation.


In comparison, there are few successful stories regarding Chinese event extraction due to special characteristics in Chinese trigger identification.

there are two major reasons for the low performance: unknown triggers 1 and word segmentation errors to known triggers.


The first rule, the non-trigger filtering rule, filters out those pseudo ones in the test set which do not frequently occur as trigger mentions in the training set.

to keep true triggers in our candidate set as many as possible, we just filter out those candidates which occur as non-triggers more than 5 times in the training set according to our validation on the development set.


In this paper we propose two novel inference mechanisms to Chinese trigger identification.

compositional semantics inside Chinese triggers and discourse consistency between Chinese trigger mentions are used to resolve two critical issues in Chinese trigger identification: unknown triggers and word segmentation errors to known triggers.


Using all of these as training queries will most certainly bias the trained model towards these professions as PRA is trained discriminatively.

training directly with this data would lead to a model that is more likely to predict professions that are popular in Freebase.


To address this limitation of context-based clustering, we first identify common types of information (main objects and their attributes) represented in listings and apply customized clustering for these types.

we define a semantic model to explicitly represent these information types, and based on the semantic model, develop two components to improve clustering -a shallow semantic parser and listing-focused clustering module.


PARSE tags the noun tokens in each listing as either head nouns or modifiers.

pARSE first assesses if a listing is "hard" to parse (line 10) based on two criteria -(1) the listing contains a long sequence of nouns (seven words or longer in our system) without any prepositions (e.g., worth shutout series 12" womens fastpitch softball fielders glove s0120 lefty); and (2) the majority of these nouns do not appear in mc words and attrs (e.g., over 70% in our system).


We analyze the error types for the wrong extraction made by our system.

for each error, we assign it to one (or more) of the following causes: (1) a cluster (and hence attribute type) was excluded due to the 1-to-1 mapping methodology described above (NOT IN MAPPING); (2) the value extracted by the system was not extracted by any of the annotators (WRONG EXT); (3) wrong information typei.e., the token belonged to a wrong cluster (WRONG TYPE); (4) incorrect tokenization for an information type (TK-<type name>).


In this paper we propose an approximate MCMC framework that facilitates efficient inference in highdegree graphical models.

we approximate the acceptance ratio in the Metropolis Hastings algorithm by replacing the exact model score with a stochastic approximation that samples from the set of relevant factors.


Although one of the benefits of MCMC lies in its ability to leverage the locality of the proposal, for some information extraction tasks this can become a crucial bottleneck.

evaluation of each sample requires computing the score of all the factors that are involved in the change, i.e.


Instead of evaluating the log-score ψ of the model exactly, this paper proposes a Monte-Carlo estimation of the log-score.

if the set of factors for a given proposal y → y is F(y, y ), we use a sampled subset of the factors S ⊆ F(y, y ) as an approximation of the model score.


The most direct approach for subsampling the set of F is to perform uniform sampling.

given a proportion parameter 0 < p ≤ 1, we select a random subset S p ⊆ F such that |S p | = p•|F|.


Further, the same value for p can result in different levels of approximation for different proposals, either unnecessarily accurate or problematically noisy.

we would prefer a strategy that adapts to the distribution of the scores in F. Instead of sampling a fixed proportion of factors, we can sample until we are confident that the current set of samples S c is an accurate estimate of the true mean of F. we maintain a running count of the sample mean E Sc [f ] and variance σ Sc , using them to compute a confidence interval I S around our estimate of the mean.


Since a subset of the mentions can be sufficiently informative for the model, we expect our stochastic MCMC approach to work well.

1 the fraction may be higher than > 5%  We use synthetic data for such a model to evaluate the quality of marginals returned by the Gibbs sampling form of MCMC.


Based on the ideas presented in the paper, we will consider additional sampling strategies.

we will explore dynamic sampling, in which we sample fewer factors during the initial, burnin phase, but sample more factors as we get close to convergence.


The meta-algorithm is both agnostic to the underlying inference algorithm that is used by the problem and maintains the exactness properties of the underlying inference scheme.

if we have an exact/approximate inference algorithm with a certain guarantees, the meta-algorithm will have the same guarantees, but with a speedup.


Suppose y * is the solution to both these programs.

for every y ∈ K [P ] , we have c T p y ≤ c T p y * and c T q y ≤ c T q y * .


In all cases, if the condition matches, the theorems guarantee that the two solutions will be the same.

applying the theorems will not change the performance of the underlying inference procedure.


The next approximate inference schemes relaxes the conditions in theorems 1 and 3 by allowing the inequalities to be violated by .

the inequality (3) from Theorem 1 now becomes The inequality (5) from Theorem 3 is similarly relaxed as follows: 3.5 Amortized inference algorithm Each exact and approximate inference approach described above specifies a condition to check whether an inference procedure should be called for a new problem.


This is done by expanding all the factors involved in the sampling/decoding of the rejected sequence x to a higher order.

while sampling or decoding the automaton using the current proposal q (t) , the contexts used in the path of the rejected sequence are replaced with higher order contexts in the new refined proposal q t+1 (x).


The result we aim at is a WordNet-style taxonomy of binary relations.

we aim at patterns that contain semantic types, such as singer sings song .


EP can be thought of as a more flexible generalization of belief propagation, which has been used several times in NLP (Smith and Eisner, 2008;Niehues and Vogel, 2008;Cromières and Kurohashi, 2009;Burkett and Klein, 2012).

eP allows for the arbitrary choice of messages (thef m ), meaning that we can use structured messages like anchored PCFGs.


Smaller models were seeded from larger models.

before training a grammar of 5 models with 1 latent bit each, we started with weights from a parser with 4 factored bits.


we consider the performance of products of up to 8 one-bit annotations.


We now consider factored models with different kinds of annotations.

we tested grammars comprising all subsets of {Lexicalized, Unlexicalized, Latent}.


This transformation saves memory by halving the number of values stored per entry, but it makes rest cost estimates worse.

the rest cost pessimistically assumes that the model will back off to unigrams immediately following the sentence fragment.


However, Kneser-Ney smoothing (Kneser and Ney, 1995) conditions lower-order probabilities on backing off.

lower-order counts are adjusted to represent the number of unique extensions an n-gram has: where c(w n 1 ) is the number of times w n 1 appears in the training data 3 .


In some cases, we are able to determine that the model will back off and therefore the lowerorder probability makes the appropriate assumption.

if vw n 1 does not appear in the model for any word v, then computing p(w n |vw n−1 1 ) will al- 3 Counts are not modified for n-grams bound to the beginning of sentence, namely those with w1 = <s>.


Where it is unknown if the model will back off, we use a language model of the same order to produce a rest cost.

there are N language models, one of each order from 1 to N .


As usual in SMT, it is assumed that the scoring function can be decomposed into a linear combination of K feature functions h k (S), each with a constant weight λ k , so The problem addressed by the decoder is the search for the stateŜ with maximal score, such that The feature functions implemented in our baseline system are identical to the ones found in the popular Moses SMT system (Koehn et al., 2007).

our decoder has the following feature functions: 1. phrase translation scores provided by the phrase table including forward and backward conditional probabilities, lexical weights and a phrase penalty (Koehn et al., 2003), 2. n-gram language model scores implemented with the KenLM toolkit (Heafield, 2011), 3. a word penalty score, 4. a distortion model with geometric decay (Koehn et al., 2003), and 5. a feature indicating the number of times a given distortion limit is exceeded in the current state.


Our model addresses the problem of lexical cohesion.

it rewards the use of semantically related words in the translation output by the decoder, where semantic distance is measured with a word space model based on Latent Semantic Analysis (LSA).


Another natural question is whether the MV-RNN can, in general, capture some of the simple boolean logic that is sometimes found in language.

can it learn some of the propositional logic operators such as and, or, not in terms of vectors and matrices from a few examples.


In our last experiment we show that the MV-RNN can also learn how a syntactic context composes an aggregate meaning of the semantic relationships between words.

the task is finding semantic relationships between pairs of nominals.


In this paper we have tackled the problem of finding a vector-space representation of words where, by construction, synonyms and antonyms are easy to distinguish.

we have defined a way of assigning sign to the entries in the co-occurrence matrix on which LSA operates, such that synonyms will tend to have positive cosine similarity, and antonyms will tend to have negative similarities.


Finally, subsective adjectives such as (the nonliterally-used) white in white wine, consitute an intermediate case: they license the inference that the property denoted by the noun holds of the individual being described, but not the property contributed by the adjective.

white wine is not white but rather a color that we would probably call some shade of yellow.


In contrast, it is not immediately obvious how these operations would fare with intensional adjectives such as former.

it is not clear what specific distributional features of the adjective would capture the effect that the ad-jective has on the meaning of the resulting modified nominal.


The counts were then transformed into Local Mutual Information (LMI) scores, an association measure that closely approximates the commonly used Log-Likelihood Ratio but is simpler to compute (Evert, 2005).

given a row element r, a column element c and a counting function C(r, c), then where C(r, c) is how many times r cooccurs with c, C(r, * ) is the total count of r, C( * , c) is the total count of c, and C( * , * ) is the cumulative cooccurrence count of any r with any c. The dimensionality of the space was reduced using Singular Value Decomposition (SVD), as in Latent Semantic Analysis and related distributional semantic methods (Landauer and Dumais, 1997;Rapp, 2003;Schütze, 1997).


We use the same evaluation metrics as used in CoNLL-2011.

for mention detection, we use precision, recall and the F-measure.


The present work is a step towards resolving abstract anaphora in written text.

we choose the interesting abstract concept issue and demonstrate the complexities of resolving this-issue anaphora manually as well as automatically in the Medline domain.


Example shown in Figure 1 captures these problems.

the word Blizzard can refer to an American game development company that develops World of Warcraft game or it could refer to a severe snowstorm.


Being closely related to the general source-channel framework (Berger and Lafferty, 1999), the model enjoys its benefits, while mitigating some of its shortcomings.

it provides for context aware expansions of the query by exploiting entity annotations on both, the document and the query side.


Therefore, we propose to minimize the sum of semantic distances along a root-to-leaf path.

when adding a new concept c z into an existing browsing hierarchy T , we try it at different positions in T .


To correct the wrong relations learned from imperfect training data, we propose to utilize user inputs in the learning process.

we formulate taxonomy construction as a user-teaching-machine-learning process.


We compare the changes between a series of snapshots of the changing taxonomy to identify the user inputs.

before a user starts editing in an interaction cycle, we represent the organization of concepts as a before matrix; likewise, after the user finishes all edits in one cycle, we represent the new organization of concepts as an after matrix.


Based on manual guidance M , we can create training data for the supervised distance learning algorithm (Section 3.1).

we transform the manual guidance into a distance matrix D = 1 − M , which is used as the training data.


This paper explores techniques to quickly derive task-specific taxonomies supporting browsing in arbitrary document sets.

we uniquely employ pair-wise semantic distance as an entry point to incrementally build browsing taxonomies.


While differences in feature behaviors between domains will hurt performance (Blitzer et al., 2008;Ben-David et al., 2009), it is not clear if the improvements in MDL algorithms can be attributed to correcting these errors, or whether they are benefiting from something else.

there are many similarities between MDL and ensemble methods, with connections to instance bag-ging, feature bagging and classifier combination.


We use the SVM implementation available in the LI-BLINEAR package (Fan et al., 2008).

we use the L 2 -regularized L 2 -loss SVM (option -s 1 in version 1.8 of LIBLINEAR, and also option -B 1 for including a standard bias feature).


For the second set of results (Table 3) where the class bias varied across the different domains, using true domains was either no different from using randomized domains, or it was significantly worse.

it was consistently significantly worse to use true domains on the AMAZON dataset.


To address this and other concerns, we modify the objective function for HM-M training.

we encode biases for representation learning by defining a set of properties φ that we believe a good representation function would minimize.


In response, we propose to bias our representation learning such that the learned representations are optimized for a specific task.

we propose a property that measures how difficult it is to predict the labels in training data, given the learned latent states.


The contribution of this paper is a semi-CRFbased approach for opinion expression extraction that leverages parsing information to provide better modeling of opinion expressions.

possible segmentations are generated by taking into account likely syntactic structure during learning and inference.


We define the boundary of these parts based on the parse tree structure.

we consider each segment unit to belong to a meaningful group defined by the span of its parent node.


We implement efficient inference using an extension of Viterbi algorithm to segments.

define V (j, y) as the largest unnormalized probability of p(s 1:j |x) with label y at the ending position j.


By using WTM, our method can capture opinion relations more precisely, especially for long-span relations.

compared with previous syntax-based methods, our method can effectively avoid noises from parsing errors when dealing with informal texts in large Web corpora.


They argued that, due to the ubiquity of food writing across cultures, ethnic groups, and social classes, studying the use of language for describing food can provide perspective on how different socioeconomic groups selfidentify using language and how they are linguistically targeted.

they showed that price affects how "authenticity" is realized in marketing language, a point we return to in §5.


We also tried to incorporate the reviews by using them in aggregate via predictions from a separate model; we found this approach to work better than the methods described above which all use features from the reviews directly in the regression model.

we use the review features in a separate model that we will describe below ( §6) to predict the price range of each restaurant.


In addition to the feature sets used for individual menu item price prediction, we used features on reviews (REVIEWS).

we used binary features for unigrams, bigrams, and trigrams in the full set of reviews for each restaurant.


In multilingual parsing, this unified input representation is required for cross-lingual syntactic transfer.

the universal tagset annotations enable an unlexicalized parser to capitalize on annotations from one language when learning a model for another.


Per sentence statistics: Another defining property of POS tags is their average count per sentence.

we focus on the verb count per sentence, which we expect be similar across languages.


If we know that the target language has a particular typological feature, we expect its universal tags to obey the given relative ordering.

we expect it to agree with ordering statistics for source languages with a similar typology.


To simplify optimization 6 we take an approach inspired by the posterior regularization method (Ganchev et al., 2010) and use the objective: (4) The above objective will attain lower values for A such that p T (c 1 , c 2 ; A) is close to the constraint set.

it will have a value of zero when the bigram distribution induced by A has the property specified in S pre .


In this work, we compare multinomial and maximum entropy (log-linear) emission models.

the max-ent emission model is: where f (x, y) is a feature function, x ranges over all 3-/178.0# 295/:2/4# 29;2/4# <6/:0.5# -32=<314# Figure 5: The Wiktionary vs. tree bank tag sets.


Instead, our objective is to show that high-performance domain WSD can be enabled with little effort by our framework.

pageRank Domain Glossaries as Graphs For each domain d ∈ D, we create an undirected graph is the set of concepts identified by term/gloss pairs in the domain glossary G d , i.e., V d := G d ; E d is the set of edges between pairs of concepts, where an edge {(t, g), (t , g )} exists if and only if t is such that t = t and t occurs in the bag of words of the gloss g of t. t is connected to all the domain senses of words used in its definition g. Graph-based WSD Given an input text, for each domain d ∈ D, we produce its bag of domain content words C d = {w 1 , w 2 , .


PPR is a variant of the popular PageRank algorithm (Brin and Page, 1998) in which the damping probability mass is concentrated on a selected number of graph nodes, instead of being uniformly distributed across all nodes.

following  we concentrate the probability mass on the nodes (t , g ) ∈ V d for which the term t is a context word, i.e., t ∈ C d .


Despite their shortcomings (e.g., they often cover a small fraction of the vocabulary of an adult speaker due to the effort involved in eliciting them), feature norms provide detailed knowledge about meaning representations and are a useful starting point for studying the integration of perceptual and textual information without being susceptible to the effects of noise, e.g., coming from image processing.

feature norms can serve as an upper bound of what can be achieved when integrating detailed perceptual information with vanilla text-based distributional models.


Our model can be applied to a variety of off-theshelf structured prediction models.

we focus on dependency parsing which is characterized by a more complicated structure compared to the IE tasks addressed by previous work.


The complete literature on domain adaptation is beyond the scope of this paper, but we refer the reader to Blitzer and Daume (2010) for a recent survey.

for parsing and POS tagging, selftraining (Reichart and Rappoport, 2007), co-training (Steedman et al., 2003) and active learning (Hwa, 2004) have been shown useful in the lightly supervised setup.


Furthermore, we add nodes with a fixed label to incorporate contexts seen in the training data.

we say that the context of a word is its POS tag and the POS tags of some set of the words around it.


We say the parses Y s are consistent with a label assignment z if for all (s, m, h) ∈ J (X) we have that z((s, m), h) = Y s (m, h).

the labels in z match the head words chosen in parse Y s .


With this notation we can write the full global decoding objective as The solution to this objective maximizes the local models as well as the global MRF, while maintaining consistency among the models.

the MRF we use in the experiments has a simple naive Bayes structure with the consensus node connected to all relevant word nodes.


While early transition-based parsers generally used greedy best-first inference and locally trained classifiers, recent work has shown that higher accuracy can be obtained using beam search and global structure learning to mitigate error propagation.

it seems that the globally learned models can exploit a much richer feature space than locally trained classifiers, as shown by Zhang and Nivre (2011).


To illustrate it, we present an example in Figure 2, in which we first use a simple threshold method to detect bursts and then analyze the effect of local smoothness.

if the amount of activities at a certain time is above a pre-defined threshold, we set its state to 1, which indicates a bursty state.


For each query, we invite two senior graduate students to manually identify their golden bursty intervals, and each bursty interval is represented as a pair of timestamps in terms of hours.

to generate the golden standard, given a query, the judges first manually generate a candidate list of external events 7 ; then for each event, they look into the tweets within the corresponding period and check whether there is a surge on the frequency of tweets.


In our work we attempt to address precisely this issue.

we consider the task of user gender prediction on YouTube and contrast two information sources: (1) the comments written by the user and (2) her social neighborhood as defined by the bipartite user-video graph.


We use a simple propagation procedure to reach the closest neighbors of a user, that is, other users "affiliated" with the same videos.


Can we learn from previously observed stimulus-response pairs (which we will refer to as exchanges)?

can we take advantage of this conversational setting and effectively use the information provided by stimulus to better predict the next word in the response?


There has been previous work in the area of humancomputer interaction that examined text entry for mobile devices (MacKenzie and Soukoreff, 2002).

one line of work looked into predictive text input, which examined input effort reduction by language prediction.


In order to predict r i+1 given r 1..i and s, for each candidate r i+1 , in principle one can marginalize over all possible completions of r 1..i+1 , and rank candidate r i+1 by that.

let P(n) be the distribution of response length, let r be a possible completion of r 1..i+1 (i.e., a response whose first i + 1 tokens match r 1..i+1 ).


At test time, we identify the most likely topic of the conversation based on s, and expect r i+1 to be generated from this topic.

where More specifically, we first train a topic model on (s, r) pairs from the training data.


We lower the weight λ s if P (t * | s) is low.

we use In order to investigate text completion in a conversational setting, we need to construct a large-scale dataset with textual exchanges among users.


We then compute the recall at different values of k. While this is a straight-forward measure to assess the overall quality of different top-k lists, it is not tailored to suit our specific task of response completion.

this measure (a) does not distinguish between (typing) savings for a short word versus a long one, and (b) does not distinguish between the correct answer being higher up in the list versus lower as long as the word is present in the top-k list.


6 On the other hand, both stimuli and responses exhibit higher uncertainty in bigram entropy (Figure 4(b)) and trigram entropy ( Figure  4(c)).

when no contexts are provided, word choices (from similarly-sized vocabularies) in D f is more evenly distributed than in D u ; but once the proceeding words are given, the next word is more predictable in D f than in D u .


Interestingly, distributions in the stimulus dataset are closer to news articles: they have a higher unigram entropy than responses, but a lower trigram entropy at comparable vocabulary sizes.

recall from Section 4 that our comments dataset contains roughly 237K repliers and 357K original com- menters.


For simplicity, we model the stimulus as a collection of unigrams.

consider the following conditional entropy: where b i is a block of N − 1 tokens in a response r, j is an arbitrary token following b i , and s k is an arbitrary token in the corresponding stimulus s for r. Note that for each b i , we consider every token in the corresponding s. a (stimulus, response) pair with m and n tokens respectively generates m * (n− N + 1) observations of (b i , j, s k ) tuples.


Interestingly, we observe F 2 > G 2 > F 3 (this trend holds for larger values of N , omitted here for clarity).

knowing both the preceding N − 1 tokens and tokens in the stimulus results lowers the uncertainty over the next token in response (bigram+stimulus entropy < bigram entropy); on the other hand, this is not as effective as knowing one more token in the preceding block (trigram entropy < bigram+stimulus entropy).


Query spelling correction has become a crucial component in modern information systems.

search engine users rely heavily on the query correction mechanism to formulate effective queries.


Similarly to (Chang et al., 2010) we collapse a two stage process into a single discriminatively trained process, by considering the output of the first stage as an intermediate latent representation for the joint learning process.

we make use of the latent structural SVM (LS-SVM) (Yu and Joachims, 2009) formulation.


Given query q, for any alignment A , where q At is the concatenation of q a 0 to q at and c At is the concatenation of c a 0 to c at .

the value of the feature (which is computed in an accumulative manner) cannot increase as the candidate is extended with a new term at any search step.


Considering the level of prediction accuracy of these tokens, and the relative frequency of the same, plus the fact that the CoNLL tabular format is not amenable to a variable number of tokens, we decided not to consider them as part of the task.

we removed the manually identified traces (*pro* and *) respectively in Chinese and Arabic Treebanks.


We can see that the fernandes and björkelund, system performance tracks very close to each other.

using gold standard parses during testing does not show much benefit in those cases.


Following Björkelund and Nugues (2011) we also implemented a decoder that works differently depending on whether m j is a pronoun or not.

for pronouns, the CF algorithm is used, otherwise the BF algorithm is used.


(2010) who reorder mentions based on mention type.

we first process proper noun phrases, then common noun phrases, and finally pronouns.


Our official combined score over all three languages is 56.35.

our score on the Chinese test set is the best among the participating teams.


We designed a system for resolving references in all three languages.

we participated in four tracks: the closed track for all three languages, and the open track for Chinese.


To improve pruning (and hence the precision of mention detection), we employ learning-based pruning, where we employ the training data to identify and subsequently discard those candidate mentions that are not likely to be coreferent with other mentions.

for each mention m k in the test set that survives heuristic pruning, we compute its mention coreference probability, which indicates the likelihood that the head noun of m k is coreferent with another mention.


We estimate the mention coreference probability of m k from the training data.

since only non-singleton mentions are annotated in OntoNotes, we can compute this probability as the number of times m k 's head noun is annotated (as a gold mention) divided by the total number of times m k 's head noun appears.


If m k 's head noun does not appear in the training set, we set its coreference probability to 1, meaning that we let it pass through the filter.

we try to be conservative and do not filter any mention for which we cannot compute the coreference probability.


To resolve a set of mentions in a document, the resolver makes multiple passes over them: in the i-th pass, it attempts to use only the rules in the i-th sieve to find an antecedent for each mention m k .

when searching for an antecedent for m k , its candidate antecedents are visited in an order determined by their positions in the associated parse tree (Haghighi and Klein, 2009).


These 10 sieves are listed under the "English" column in Table 2.

we leave out the Alias sieve and the Lexical Chain sieve, which compute semantic similarity using information extracted from WordNet, Wikipedia, and Freebase.


This is done based on the observation that all mentions in the same coreference chain should agree in gender, number, and animacy.

given a training text, if one of the mentions in a coreference chain is heuristically labeled with grammatical information, we automatically annotate all the remaining mentions with the same grammatical attribute values.


Finally, we automatically create six word lists, containing (1) animate words, (2) inanimate words, (3) male words, (4) female words, (5) singular words, and (6) plural words.

we populate these word lists with the grammatically annotated mentions from the previous step, where each element of a word list is composed of the head of a mention and a count indicating the number of times the mention is annotated with the corresponding grammatical attribute value.


To exploit lexical information, we first compute lexical probabilities.

for each pair of mentions m j and m k in a test text, we first compute two probabilities: (1) the string-pair probability (SP-Prob), which is the probability that the strings of the two mentions, s j and s k , are coreferent; and (2) the head-pair probability (HP-Prob), which is the probability that the head nouns of the two mentions, h j and h k , are coreferent.


The first extension aims to improve the precision of the sieve approach.

before applying any sieve, we check whether SP-Prob(m j ,m k ) ≤ t SP L or HP-Prob(m j ,m k ) ≤ t HP L for some thresholds t SP L and t HP L .


The second extension, on the other hand, aims to improve recall.

we create a new sieve, the Lexical Pair sieve, which we add to the end of the sieve pipeline and which posits two mentions m j and m k as coreferent if SP-Prob(m j ,m k ) ≥ t SP U or HP-Prob(m j ,m k ) ≥ t HP U .


We associate with condition i a parameter λ i , which is a binary value that controls whether condition i should be removed or not.

if λ i =0, condition i will be dropped from the corresponding rule.


Before discussing the parameter estimation algorithms, recall from the introduction that one of the distinguishing features of our approach is that we build genre-specific resolvers.

for each genre of each language, we (1) learn the lexical probabilities from the corresponding training set; (2) obtain optimal parameter values Θ 1 and Θ 2 for the development set using parameter estimation algorithms 1 and 2 respectively; and (3) among Θ 1 and Θ 2 , take the one that yields better performance on the development set to be the final set of parameter estimates for the resolver.


This algorithm learns the two sets of parameters in a sequential fashion.

it first tunes the lexical probability thresholds, assuming that all the rule relaxation parameters are set to one.


To do so, we apply the backward elimination feature selection algorithm, viewing each condition as a feature that can be removed from the "feature set".

all the parameters are initially set to one, meaning that all the conditions are initially present.


We adopted Parameter Correct Incorrect tC no problem; the same that; that idea tHP L (people,that); (both of you,that) (ours,they); (both of you,us) tSP L (first,first); (the previous year,its) (China,its); (Taiwan,its) tHP U (The movie's,the film); (Firestone,the company's) (himself,he); (My,I) tSP U (Barak,the Israeli Prime Minister); (she,the woman); (Taiwan,the island) (Kostunica,the new Yugoslav President)  a hybrid approach to coreference resolution, which combined the advantages of rule-based methods and learning-based methods.

we proposed two extensions to Stanford's multi-pass sieve approach, which involved the incorporation of lexical information using machine learning and the acquisition of genre-specific resolvers.


In the current implementation, we focus on a subset of mentions to further improve the mention detection stage of the baseline system.

we fix mentions starting with a stop word and mentions ending with a punctuation mark.


This paper focuses on adapting BART to new languages, discussing the problems we have encountered and the solutions adopted.

we propose a novel entity-mention detection algorithm that might help identify nominal mentions in an unknown language.


We plan to take a generic solution for different languages rather than customized approach to some languages with special resources.

our official system didn't take any special processing for data of different languages but used the same strategy and feature sets for all three languages.


Our research result in this paper will answer this ques- tion.

we address this online active domain adaptation problem by extending the online active learning framework in (Cesa-Bianchi et al., 2006) to consider active label acquirement in both domains.


Multi-view learning techniques have recently been employed in domain adaptation (Tur, 2009;Chen et al., 2011).

instead of using data with conditional independent views assumed in standard multi-view learning (Blum and Mitchell, 1998),  and Chen et al.


Otherwise, we make the query decision based on the two predictors' uncertainty (i.e., the inverse of the prediction confidence m k ) over the selected instance.

we sample two numbers, one for each view, according to where b is a prior hyperparameter, specifying the tendency of querying labels.


Follow the principle suggested in the multi-view active learning work (Muslea et al., 2000(Muslea et al., , 2002Zhou, 2008, 2010) that querying labels for contention points (instances on which different views predict different labels) can lead to superior information gain than querying uncertain points, we identify the non-redundant contention points from the two domains for label acquisition.

there are three cases: (1) If only one of the instances is a contention point, we query its label with probability P c (Eq (1)) when the instance is from the target domain, and query its label with probability 1 − P c when the instance is from the source domain.


Proofs of relationships between the level of Kappa agreement and the difference in performance between consecutive models are presented.

if the Kappa agreement between two models exceeds a threshold T (where T > 0), then the difference in F-measure performance between those models is bounded above by 4(1−T) T in all cases.


In the remainder of this section we describe the mathematical foundations for stopping methods based on stabilizing predictions.

we will prove that even in the worst possible case, if the Kappa agreement between two subsequently learned models is greater than a threshold T , then it must be the case that the change in performance between these two models is bounded above by 4(1−T ) .


We presented proofs of relationships between the level of Kappa agreement and the difference in performance between consecutive models.

if the Kappa agreement between two models is at least T, then the difference in F-measure performance between those models is bounded above by 4(1−T ) T .


All frequency ratios (that of f (x), f (y), and f (x, y)) being equal, bigrams composed of low frequency words get higher score than those composed of high frequency words.


In contrast, we discuss 1) employing only the annotated data in a supervised manner, while entirely ignoring the unannotated data, and 2) directly learning to predict morph boundaries given their local sub-string contexts instead of learning the morph lexicons.

we employ conditional random fields, a popular discriminative log-linear model for segmentation.


In contrast to learning morph lexicons (Poon et al., 2009;Kohonen et al., 2010), we study morphological segmentation by learning to directly predict morph boundaries based on their local substring contexts.

we apply the linearchain conditional random field model, a popular discriminative log-linear model for segmentation presented originally by Lafferty et al.


We have presented an empirical study in datadriven morphological segmentation employing supervised boundary prediction methodology.

we applied conditional random fields, a discriminative log-linear model for segmentation and tagging.


, T } and all K 2 possible (y t , y t−1 ) label pairs.

m i is a length O(T K 2 ) vector with entries: (4) Stacking all these m i , we let m represent the O(nT K 2 ) vector [m 1 , .


To encourage every sentence to contain at least one verb, we can penalize if the expected number of verbs under the q distribution is less than 1.

if "verb" is represented by tag number v, for sentence i we penalize unless: In the notation of Equation 5, these penalties correspond to: an n-row A matrix, where row i has −1's to select exactly the portion of m from Equation 7, and a limit b = −1.


The most important aspect of EGD for us is that a variable's update formula takes a multiplicative rather than an additive form.


Thankfully, we can show these EGD updates are equivalent to following the gradient on a much smaller set of values.

letting F represent the dimension of m, which for example is O(nT K 2 ) for linear chains, we have the following result.


We train a second-order CRF using the methods described in Section 3 and report results for a test set consisting of instances l + 1 through n. For each task we define a symmetric similarity function on the task's vertices V , sim : V × V → R, and build the graph based on its values.

denoting the k nearest neighbors (NN) of node u by N k (u), we use the following mutual k-NN criterion to decide which edges to include: Entries in the final edge weight matrix are: We experiment on ten languages.


The emission features take a similar form, but with different meanings for the f (x t ) indicator functions.

there is one indicator for each pixel location, with value 1 if the pixel is turned on.


The first baseline is graph propagation (GP).

we start from uniform posteriors for all the unlabeled nodes in the graph, then for each tag/letter k and each node v we apply the gradient update: until convergence.


However, to the best of our knowledge, there are no approaches that apply boosting to learning algorithms for structured prediction.

if we can successful apply boosting to learning algorithms for structured prediction, we expect to obtain higher accuracy.


Under the model, it has probability An equivalent definition of an R-HMM is given by organizing the parameters in matrix form.

an R-HMM has parameters x is a row vector, T b|a ∈ R m×m is a matrix, and f a ∈ R m is a row vector, defined for all a, b ∈ [l] and x ∈ [n].


However, our approach is specifically motivated by the perspective that both these representations of a sentence-a sequence of tokens and a tree of dependency relations-are equally meaningful when considering its underlying fluency and integrity.

models for compressing a token sequence must also account for the compression of its dependency representation and vice versa.


However, unlike the bag-of-tokens scenario, these output structures cannot be constructed efficiently due to their interdependence.

we need to maintain the following conditions in order to obtain an interpretable token sequence y: • Trigram variables y ijk must be non-zero if and only if their corresponding word variables x i , x j and x k are non-zero.


3 Note that this does not always hold for n-grams of order n > 2 due to the way terminal n-grams featuring START and END are defined.

in a valid linear ordering of tokens and ∀r ∈ 1 .


The extraction by Knight and Marcu (2000) of a dataset of natural compression instances from the Ziff-Davis corpus spurred interest in supervised approaches to the task (Knight and Marcu, 2002;Riezler et al., 2003;Turner and Charniak, 2005;McDonald, 2006;Unno et al., 2006;Galley and McKeown, 2007;Nomoto, 2007).

mcDonald (2006) expanded on Knight & marcu's (2002) transitionbased model by using dynamic programming to recover optimal transition sequences, and Clarke and Lapata (2006a) used ILP to replace pairwise transitions with trigrams.


Finally, the results show that our methods greatly improve on IR baseline, e.g., BM25, by 40%, and on previous reranking models, up to 10%.

differently from our previous work such models can effectively use NERs and the output of different automatic modules.


Given the ability of PTK to generate a rich set of structural features from a relatively flat shallow tree representation, we propose to use dependency relations between words to derive an alternative structural model.

we use a variation of the dependency tree, where dependency relations are altered in such a way that the words are always at the leaf level.


Below we propose a novel strategy to establish relational links using named entities extracted from the answer along with question focus and category classifiers.

we use a question category to link the focus word of a question with the named entities extracted from the candidate answer.


(Quarteroni et al., 2012;Damljanovic et al., 2010;Bunescu and Huang, 2010), we opt for the latter approach where tree kernels handle automatic feature engineering.

to detect the question focus word we train a binary SVM classifier with tree kernels applied to the constituency tree representation.


Hence, we propose to exploit a question category (automatically identified by a question type classifier) along with named entities found in the answer to establish relational links between the tree structures of a given q/a pair.

once the question focus and question category are determined, we link the focus word w f ocus in the question, with all the named entities whose type matches the question class.


To train our reranking models we used SVM-light-TK 7 , which encodes structural kernels in SVMlight (Joachims, 2002) solver.

we use PTK on the relational tree structures combined with the polynomial kernel of degree 3 applied to the feature vectors.


Our morphological Recursive Neural Network (morphoRNN) is similar to (Socher et al., 2011b), but operates at the morpheme level instead of at the word level.

morphemes, the minimum meaning-bearing unit in languages, are modeled as real-valued vectors of parameters, and are used to build up more complex words.


By assuming that these reference vectors are right, the goal of the model is to construct new representations for morphologically complex words from their morphemes that closely match the corresponding reference ones.

the structure of the contextinsensitive morphoRNN (cimRNN) is the same as the basic morphoRNN.


Motivated by these limitations, we propose a context-sensitive morphoRNN (csm-RNN) which integrates RNN structures into NLM training, allowing for contextual information being taken into account in learning morphemic compositionality.

we adopt the NLM training approach proposed in  to learn word embeddings, but build representations for complex words from their morphemes.


Compositional and distributional semantic models seem to provide complementary solutions for solving the same problem, that of assigning a proper "meaning" to a text segment.

while compositional models deal with the recursive nature of the language, providing a way to address its inherent ability to create infinite sentences from finite resources (words), they leave words as unexplained primitives whose meanings have somehow already been set before the compositional process.


However, although it is generally true that multiplying − − → run with − −− → horse will filter out most of the components of − − → run that are irrelevant to 'dissolve' (since the 'dissolve'-related elements of − −− → horse should have values close to zero) and will produce a disambiguated version of this verb under the context of 'horse', it is not at all clear if this vector will also constitute an appropriate representation for the meaning of the intransitive sentence 'horse runs'.

here we have two tasks taking place at the same time: (a) disambiguation of the ambiguous word given its context; and (b) composition that produces a meaning vector for the whole sentence.


We are now able to disambiguate the sense of a target word w t given a context C by calculating a context vector − → c for C as above, and then comparing this with every 2nd-order vector of w t ; the word is assigned to the sense that corresponds to the closest 2nd-order vector.

− − → s pref = arg min where S is the set of 2nd-order vectors for w t and d( − → u , − → v ) the vector distance metric we use.


The parameters of the clustering scheme are optimized on the noun set of SEMEVAL 2010 Word Sense Induction & Disambiguation Task (Manandhar et al., 2010).

when using HAC one has to decide how to measure the distance between the clusters, which is the merging criterion applied in every iteration of the algorithm, as well as the measure between the data points, i.e.


Instead, the comparison of a sentence with a "landmark" sentence is simply based on disambiguated versions of the verbs alone.

the main verb and the landmark verb are disambiguated given the context (subjects, objects, and adjectives that modify them) according to Equation 1; this produces two 2nd-order vectors, one for the main verb and one for the landmark.


These are strong indications that the quality of composition is not crucial for disambiguation tasks of this sort, whose only achievement is that they measure the disambiguation sideeffects generated by the compositional process.

the practice of evaluating the quality of composition by using disambiguation tasks is problematic.


Our goal in this paper is to improve the state of the art in stance classification of online debates, focusing in particular on ideological debates.

we present two extensions, one linguistic and the other extra-linguistic, to the state-of-the-art supervised learning approach to this task proposed by Anand et al.


The negation value of Pattern 10 is POS rather than NEG, reflecting the fact that not like does not appear in a negative context.

the sentiment value needs to be flipped if the verb is negated, and so may the negation value.


Unlike c b , however, the features employed by c s are created from the learned patterns.

from each pattern we create one binary feature whose value is 1 if and only if the corresponding pattern is applicable to the training post under consideration.


Hence, we define confidence for classifier c i by the interval [conf i l , conf i u ], where conf i l < 0 and conf i u > 0 are signed distances from the hyperplane defining c i .

we say that a point p is confidently classified by c i if and only if p lies outside the interval defined by conf i l and conf i u .


On the other hand, this is not necessarily the case in our proposal, because two same-author posts can be classified using different combinations.

aCs and our combination method are not the same.


To do this, we train a binary author-agreement classifier on the training set to generate A similar for a.

each training instance corresponds to a pair of authors in the training set having one of two class labels, agree (i.e., authors have the same stance) and disagree (i.e., authors have opposing stances).


while existing approaches to debate stance classification have primarily employed lexico-syntactic features, to our knowledge this is the first attempt to employ FrameNet for this task to induce features that aim to capture the meaning and provide semantic generalizations of a sentence.


However, as the domain of a database grows, it becomes increasingly impractical to collect completely, and increasingly unlikely that all the elements intended for the database are explicitly mentioned in the source corpus.

common-sense facts are rarely explicitly mentioned, despite their abundance.


A large body of work has been devoted to creating such knowledge bases.

open IE systems such as TextRunner (Yates et al., 2007), ReVerb (Fader et al., 2011), ollie (Mausam et al., 2012), and NELL (Carlson et al., 2010) have tackled the task of compiling an open-domain knowledge base.


There have been a number of systems aimed at automatically extending these databases.

given an existing database, they propose new relations to be added.


To simulate unseen facts, we construct training instances by predicting the plausibility of a fact held out from the database.

if our database consists of Negative examples are likewise constrained to not occur in the database, as are the facts used in their construction.


Considering the low level of prediction accuracy of these tokens, and their relative low frequency, we decided to consider predicting traces in trees out of the scope of this study.

we removed the manually identified traces and function tags from the Treebanks across all three languages, in all the three -training, development and test partitions.


For the first two mentions, clearly the top entity candidate is incorrect; hence approaches that rely on a fixed alignment will perform poorly.

since "Washington State" mention is not compatible with the top-ranked entities of the first two mentions (Washington, D.C. and Car Wash respectively), approaches that do not modify the ranking during inference may not resolve them.


The quality of alignment and the coreference predictions for a document is influenced by the quality of the mentions in the document.

ACE contains a large number of broadcast news documents, many of which consist of transcribed data containing noise in the form of incomplete sentences and disfluencies.


3 The reasoning is based on the observations that in the arc-eager system, new arcs (h, l, m) can be derived iff the following conditions hold: (a) There is no existing arc (h , l , m) such that h = h, and (b) Either both h and m are on the buffer, or one of them is on the buffer and the other is on the stack.

: (a) once a word acquires a head (in a Left-Arc or Right-Arc transition) it loses the ability to acquire any other head.


The CVB algorithm for the PCFG model keeps the dependencies between the parameters and the hidden parse trees in an exact fashion: We factorise Q(t) by breaking only the weak dependencies between parse trees, while keeping the inside dependencies intact, as we don't make further assumptions about Q(t) for each t. By the above factorisations, we compute the negative variational free energy −F(Q(t)Q(θ|t)) as follows: Maximizing −F(Q(t)Q(θ|t)) requires to update Q(θ|t) and Q(t) in turn.

q(θ|t) is set equal to the true posterior P (θ|w, t, α): Finally, we update the approximate posterior for each parse tree t by using the mean field method in the collapsed space: The inner term P (w i , t i |w ¬i , t ¬i , α) in the above equation is just the unnormalized collapsed Gibbs sampling in (5).


In this work we seek to remove these barriers to entry by generating word embeddings for over a hundred languages using state-of-the-art techniques.

our contributions include: • Word embeddings -We will release word embeddings for the hundred and seventeen languages that have more than 10,000 articles on Wikipedia.


• Well studied -Wikipedia is a prolific resource in the literature, and has been used for a variety of problems.

wikipedia is well suited for multilingual applications (Navigli and Ponzetto, 2010 To process wikipedia markup, we first extract the text using a modified version of the Bliki en-gine 1 .


The approach that we propose attempts to take advantage of these resources, but for training purposes.

given the training sentences in Spanish, they are translated into a new language (French in this case) by using several web translators.


As the figure shows, the obtained graph of words (where the arcs are labeled with words and weighted with the normalized counters) represents a language which is a generalization of the individual translations of the original utterance.

this process is a Grammatical Inference mechanism that represents sentences with characteristics that are similar to those used to build the graph.


This process takes as its input an utterance and outputs a weighted graph of words, which represents the probability distribution p(W t |A).

each full path in the graph of words (from the initial to the ending node) is a candidate translation of the input utterance, and is weighted with the probability of the translation given the utterance.


The approach has the advantage of not requiring bilingual parallel data, but it suffers from issues typical of distributional similarity metrics.

it produces paraphrases that share the same or similar contexts but are related in ways that do not always characterise paraphrasing, such as antonymy.


We start the process at e 1 and we need to make sure that the pivot phrases f ∈ F align back to the input language, producing the paraphrase e 2 , and to the target language, producing the sense tag q.

to avoid computing the distribution p(e 2 , q|f ) -which would require a trilingual parallel corpus -we assume that e 2 and q are conditionally independent on f : we assume that pivot phrases generate paraphrases and sense tags independently.


By using word alignment on topic model results, we can easily model polysemy and extract topic-dependent lexicons.

let w e be an English word and w f be a French word.


The ROC curve lets us observe the change in Recall as we gradually accept more translation pairs as dictionary candidates.

it measures the true positive rate (i.e.


We design a simple likelihood ratio test to operationalize this assumption.

we consider the probabilities p 1 = P r(pm|head; pros) and p 2 = P r(pm|head; cons), where p 1 (p 2 ) denotes the probability in a corpus of pros (cons) lists that pm occurs as pre-modifier with the head noun head.


To evaluate our approaches, we manually annotate a subset of the crawled reviews.

we randomly sample subsets of 150 hotel and 150 camera reviews that do not overlap with the foreground corpora.


To understand the reasons for the insignificant effect, we perform a mistake analysis of the false negatives in scenario B.

we compare the false negatives with and without variant aggregation.


Despite some shared properties, MSVs are a heterogeneous group, with different types of verbs exhibiting different developmental patterns.

a wealth of research shows that children produce Desire verbs, such as want and wish, earlier than Belief verbs, such as think and know (Shatz et al., 1983; Bartsch and Wellman, 1995;Asplin, 2002;Perner et al., 2003;de Villiers, 2005;Papafragou et al., 2007;Pascual et al., 2008).


Some explanations for this pattern posit that differences in the syntactic usages of Desire and Belief verbs underlie the observed developmental lag of the latter (de Villiers, 2005;Pascual et al., 2008).

desire verbs occur mostly with an infinitival SC (as in I want (her) to leave), while Belief verbs occur mostly with a finite SC (a full tensed embedded clause, as in I think (that) she left).


The behaviour of the model can thus be attributed to its probabilistic learning mechanisms in conjunction with the distributional properties of the input.

we investigate how the model's learning mechanism interacts with the distributions of several classes of verbs -including Belief, Desire, Perception, Communication, and Action -in the finite and infinitival SC syntax to produce the observed pattern of acquisition of Desire and Belief verbs.


(2012) model is a sequence of frames, where each frame is a collection of syntactic and semantic features representing what the learner might extract from an utterance s/he has heard paired with a scene s/he has perceived.

we consider syntactic properties, including syntactic pattern, argument count, and complement type, as well as semantic properties, including event primitives and event participants.


This mechanism basically entails that the model may "misinterpret" an input frame containing an MSV as focusing on the semantics of the action in the sentential complement.

when receiving an input frame with an MSV, as in Table 1, there is a probability p that the frame is perceived with attention to the semantics corresponding to the physical action verb (here, make).


Our focus here is on learning the Belief and Desire classes; however, we include verbs from other classes to have a realistic context of MSV acquisition in the presence of other types of verbs.

we include (physical) Action verbs because of their frequent usage in CDS, and we include Communication and Perception groups because of their suggested role in the acquisition of MSVs (Bloom et al., 1989;de Villiers, 2005).


In an adaptation of the lexicon of Barak et al., we make minimal assumptions about shared semantics across verb classes.

to encode suitable semantic distinctions among MSVs, and between MSVs and other verbs, we aimed for a representation that would capture reasonable as-sumptions about high-level similarities and differences among the verb classes.


Psycholinguistic studies have used variations of a novel verb prediction task to examine how strongly children (or adults) have learned to associate the various syntactic and semantic properties of a typical MSV usage.

the typical Desire verb usage combines desire semantics with an infinitival SC syntax, while the typical Belief verb usage combines belief semantics with a finite SC syntax.


Moreover, we replicate the experimental results of PCG without encoding any conceptual or syntactic differences in difficulty between the two types of verbs.

because the representation of Desire and Belief classes in our experiments does not build in a bias due to the ease of processing Desire verbs, the differential results in the model must be due to the interaction of the different distributional patterns in CDS (see Table 2) and the processing approach of the model.


Suppose the tokenized output sentence H of a grammatical error correction system given the above sentence is: There is no doubt, tracking system has brought many benefits in this information age .

the set of system edits is e = {a doubt → doubt}.


This was also the practice adopted in HOO 2011 and HOO 2012.

after the teams submitted their system output and the error annotations on the test essays were released, we allowed the teams to propose alternative answers (gold-standard edits), to be submitted within four days after the initial error annotations were released.


We use the POS tag and the shallow parser output to identify the set of candidates that are input to the classifiers.

for nouns, we collect all words tagged as NN or NNS.


", if the word "applications" is changed to "application" (Nn error) by a correction module, then the following auxiliary verb "do" should be revised to "does" (SVA error) accordingly.

if a mistake is introduced by a component in the prior step, subsequent analyses are most likely affected negatively.


Because the size of the supplied training data is too small to train an effective SMT system, we used additional data from the Cambridge Learner Corpus 1 (CLC).

we derived new pairs of incorrect and correct sentences using the publicly available scripts from the First Certificate in English (FCE) (Yannakoudakis et al., 2011) and others from the International English Language Testing System (IELTS) examinations, which include mainly academic writing.


In our systems, all out-of-vocabulary words were directly transferred from source to target.

whenever our system encounters a word it has not seen previously, it keeps it unchanged.


It is also worth mentioning what can be considered a general rule for the combination of these two approaches: a system based on the mixed approach should obtain better results if each part of the system is applied according to its "competence".

some problems are better solved by the application of the rules-like the rules for choosing the correct allomorph of the article "a" vs. "an", while other problems are better solved by the usage of ML methods-such as deciding the presence or absence of a definite or an indefinite determiner.


This could be reformulated as: we should take the last n words of the (unique) path from the root to the current word.

we start from the root and reach one of the dependent words.


To process the prepositions, we used the training data provided by the organizers.


It also means that we are only able to correct "single words to single words".

it is not possible to substitute two words for one, and vice versa.


However, the main difference between our system and previous ones is that we do not select our confusion sets in advance of statistical modelling.

although the confusion sets we used were based on POS tagsets, there was no classifying or learning to discriminate among members of a confusion set before the task.


Verb forms in English often consist of a closed class word w, followed by a main verb, the form of which combines with the particular w to indicate the tense and aspect.

w functions as a verb form marker, and doesn't carry other information.


In our work, it is a wrapper feature selection task.

we have to select a combination of features that perform well together rather than make sure each of them behaves well.